Final Presentation

Tong Wang

1. Automatic Article Screening in Systematic Review

2. Compression Algorithm on Document Classification

Automatic Article Screening

• Review Question: Vitamin C for preventing and treating common cold?

• Data set: 17 References articles.664 Not references articles.

Problem Definition

• Input : document d classes(c1 = Reference, c2 = not a reference)• Output: predicted class of d• Goal: find all articles belong to c1(Reference)

Build Features

• “Bag of Words” assumption: the order of words in a document can be neglected

• Preprocessing: tokenization, lemma, remove stop words, remove some part of speech.

• Need a step: Name Entity Recognizer(NER), it labels sequences of words which are the name of things. It is implemented by linear chain Conditional Random Field(CRF)

Build features

• Vector space model• Extract vocabulary over all articles.• Each document can be represented by a vector,

value in each dimension is the word frequency in this article

• N = size of vocabulary w1, w2, w3, w4… wNd1 1 0 2 0 … 0d2 0 1 0 0 … 0

Naïve Bayes

Logistic Regression

Discuss

• Define loss matrix, give high penalty for false negative.

• Another way is to use Cosine distance to compute similarity between articles. Wiki def:

• Use other nlp probability model, like LSA, LDA

Compression

• The basic idea is the data contains patterns that occur with a certain regularity will be compressed more efficiently

• It is generally inexpensive

• d(x, y) = c(x y)/(c(x) + c(y))• x: A document • c(x) : size of compressed file x• xy: the file obtained by concatenating x and y• d(x,y) – 1/2 >= 0

X

y

xy

C(x)

C(y)

C(xy)

Compression Matrix

a1 a2 a3 a4….b1 d(b1, a1) d(b1, a2)b2 d(b2, a1) d(b2, a2)b3b4…

Experiments

• Two groups of drug review(ADHD) articles.• Two groups of machine learning articles.• Each group has 15 articles• Intuitively d(ADHD, ADHD) < d(ADHD, machine learning)d(machine learning, machine learning) < d(ADHD, machine learning)

Future work

• More experiments• Compare cosine(x, y) and d(x, y)